{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 模型复杂度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston()\n",
    "X = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(506, 13)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "maxSampleLeaf = 506\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in range(1, maxSampleLeaf+1):\n",
    "    dt_reg = DecisionTreeRegressor(min_samples_leaf=i)\n",
    "    dt_reg.fit(X_train, y_train)\n",
    "    y_train_predict = dt_reg.predict(X_train)\n",
    "    train_scores.append(r2_score(y_train, y_train_predict))\n",
    "    test_scores.append(dt_reg.score(X_test, y_test))\n",
    "    \n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], train_scores, label=\"train\")\n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], test_scores, label=\"test\")\n",
    "plt.xlim(506, 1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "maxSampleLeaf = 100\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in range(1, maxSampleLeaf+1):\n",
    "    dt_reg = DecisionTreeRegressor(min_samples_leaf=i)\n",
    "    dt_reg.fit(X_train, y_train)\n",
    "    y_train_predict = dt_reg.predict(X_train)\n",
    "    train_scores.append(r2_score(y_train, y_train_predict))\n",
    "    test_scores.append(dt_reg.score(X_test, y_test))\n",
    "    \n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], train_scores, label=\"train\")\n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], test_scores, label=\"test\")\n",
    "plt.xlim(maxSampleLeaf, 1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "maxSamplesSplit = 300\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in range(2, maxSamplesSplit+1):\n",
    "    dt_reg = DecisionTreeRegressor(min_samples_split=i)\n",
    "    dt_reg.fit(X_train, y_train)\n",
    "    y_train_predict = dt_reg.predict(X_train)\n",
    "    train_scores.append(r2_score(y_train, y_train_predict))\n",
    "    test_scores.append(dt_reg.score(X_test, y_test))\n",
    "    \n",
    "plt.plot([i for i in range(2, maxSamplesSplit+1)], train_scores, label=\"train\")\n",
    "plt.plot([i for i in range(2, maxSamplesSplit+1)], test_scores, label=\"test\")\n",
    "plt.xlim(maxSamplesSplit, 2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
   "language": "python",
   "name": "python36864bitanacondaconda085db2e8b14649f4b32196068f7124e9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}